Learning TSK Fuzzy Rules from Data Streams
Abstract
Learning from data streams has received increasing attention in recent years, not only in the machine learning community but also in other research fields, such as computational intelligence and fuzzy systems. In particular, several rule-based methods for the incremental induction of regression models have been proposed. In this paper, we develop a method that combines the strengths of two existing approaches rooted in different learning paradigms. Our method induces a set of fuzzy rules, which, compared to conventional rules with Boolean antecedents, has the advantage of producing smooth regression functions. To do so, it makes use of an induction technique inspired by AMRules, a very efficient and effective learning algorithm that can be seen as the state of the art in machine learning. We conduct a comprehensive experimental study showing that a combination of the expressiveness of fuzzy rules with the algorithmic concepts of AMRules yields a learning system with superb performance.
Cite
Text
Shaker et al. "Learning TSK Fuzzy Rules from Data Streams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_34Markdown
[Shaker et al. "Learning TSK Fuzzy Rules from Data Streams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/shaker2017ecmlpkdd-learning/) doi:10.1007/978-3-319-71246-8_34BibTeX
@inproceedings{shaker2017ecmlpkdd-learning,
title = {{Learning TSK Fuzzy Rules from Data Streams}},
author = {Shaker, Ammar and Heldt, Waleri and Hüllermeier, Eyke},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {559-574},
doi = {10.1007/978-3-319-71246-8_34},
url = {https://mlanthology.org/ecmlpkdd/2017/shaker2017ecmlpkdd-learning/}
}